# Co-Design of Memory-Storage Systems for Workload Awareness with Interpretable Models

arXiv: https://arxiv.org/abs/2603.15571 · HTML: https://mlsanity.com/paper/2603.15571/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [SuperBench: Improving Cloud AI Infrastructure Reliability with Proactive Validation](https://mlsanity.com/paper/2402.06194/) (2024)
- [Error Characterization, Mitigation, and Recovery in Flash Memory Based Solid-State Drives](https://mlsanity.com/paper/1706.08642/) (2017)
- [Reducing Solid-State Drive Read Latency by Optimizing Read-Retry](https://mlsanity.com/paper/2104.09611/) (2021)
- [Improving 3D NAND Flash Memory Lifetime by Tolerating Early Retention Loss and Process Variation](https://mlsanity.com/paper/1807.05140/) (2018)
- [Improving the Reliability of Next Generation SSDs using WOM-v Codes](https://mlsanity.com/paper/2208.00772/) (2022)
- [Performance Characterization of NVMe Flash Devices with Zoned Namespaces (ZNS)](https://mlsanity.com/paper/2310.19094/) (2023)
- [A Bring-Your-Own-Model Approach for ML-Driven Storage Placement in Warehouse-Scale Computers](https://mlsanity.com/paper/2501.05651/) (2025)

## Abstract (arXiv)

Solid-state storage architectures based on NAND or emerging memory devices (SSD), are fundamentally architected and optimized for both reliability and performance. Achieving these simultaneous goals requires co-design of memory components with firmware-architected Error Management (EM) algorithms for density- and performance-scaled memory technologies. We describe a Machine Learning (ML) for systems methodology and modeling for co-designing the EM subsystem together with the natural variance inherent to scaled silicon process of memory components underlying SSD technology. The modeling analyzes NAND memory components and EM algorithms interacting with comprehensive suite of synthetic (stress-focused and JEDEC) and emulation (YCSB and similar) workloads across Flash Translation abstraction layers, by leveraging a statistically interpretable and intuitively explainable ML algorithm. The generalizable co-design framework evaluates several thousand datacenter SSDs spanning multiple generations of memory and storage technology. Consequently, the modeling framework enables continuous, holistic, data-driven design towards generational architectural advancements. We additionally demonstrate that the framework enables Representation Learning of the EM-workload domain for enhancement of the architectural design-space across broad spectrum of workloads.